|Publication number||US7043057 B2|
|Application number||US 10/736,454|
|Publication date||May 9, 2006|
|Filing date||Dec 15, 2003|
|Priority date||Jun 7, 2001|
|Also published as||US6674878, US7173707, US7411681, US20020186865, US20040156531, US20060262312, US20070216904, WO2002101340A2, WO2002101340A3|
|Publication number||10736454, 736454, US 7043057 B2, US 7043057B2, US-B2-7043057, US7043057 B2, US7043057B2|
|Inventors||James E. Retterath, Robert A. Laumeyer|
|Original Assignee||Facet Technology, Corporation|
|Export Citation||BiBTeX, EndNote, RefMan|
|Patent Citations (53), Non-Patent Citations (23), Referenced by (26), Classifications (22), Legal Events (7)|
|External Links: USPTO, USPTO Assignment, Espacenet|
This application is a continuation application of U.S. application Ser. No. 09/918,375, filed Jul. 30, 2001 and entitled, “SYSTEM FOR AUTOMATED DETERMINATION OF RETROREFLECTIVITY OF ROAD SIGNS AND OTHER REFLECTIVE OBJECTS,” now issued as U.S. Pat. No. 6,674,878, which claims the benefit of the contents and filing date accorded to U.S. Provisional Patent Application No. 60/296,596, filed Jun. 7, 2001 and entitled, “METHOD AND APPARATUS FOR DETERMINING RETROREFLECTIVITY FROM A MOVING PLATFORM.”
The present invention relates to the field of automated electronic measurement and object identification systems. More particularly, the present invention is directed to methods and an apparatus for the automated determination of certain characteristics of desired reflective objects (such as road signs) and classifying the reflective objects as to the level of retroreflectivity.
Safe motor vehicle travel during low light and nighttime conditions requires that directional, regulatory and cautionary information displayed upon road signs and markers be clearly visible to a vehicle operator traveling at a reasonable velocity down a roadway. Various kinds of reflective sheeting, decals and paints are used on road signs and markers to enhance the readability and perception of information displayed during low light and nighttime conditions. Unfortunately, the effectiveness of these reflective materials tends to deteriorate over time.
Adequate nighttime and low light visibility of roadway signs by vehicle operators is best associated and most impacted with the retroreflectance properties of the individual signs. Retroreflectivity (defined as the ability of a material to reflect incident light back towards its source), specified in candelas per lux per square meter (cd/lux/m2), is an important characteristic utilized by transportation agencies to assess the nighttime visibility of road signs.
Generally, highway and street maintenance departments do not systematically evaluate the deterioration of the reflective materials used on road signs and markers. If inspections of road signs or markers are performed, they are typically accomplished by having inspectors manually position a handheld retroreflectometer directly on the surface of a sign in order to determine a retroreflectivity value for that sign. When there are a large number of road signs or markers (sometimes referred to as traffic control devices or TCDs) in a given jurisdiction, the task of manually inspecting all of these road signs and markers can be time consuming and expensive.
One technique for determining retroreflectivity which does not require that a retroreflectometer be placed directly on a sign is described in U.S. Pat. No. 6,212,480 entitled, “APPARATUS AND METHOD FOR DETERMINING PRECISION REFLECTIVITY OF HIGHWAY SIGNS AND OTHER REFLECTIVE OBJECTS UTILIZING AN OPTICAL RANGE FINDER INSTRUMENT,” issued Apr. 3, 2001 to Dunne. The Dunne patent relates to a device commercialized by the assignee thereof and marketed as the “Impulse RM” retroreflectometer by Laser Technology, Inc., of Englewood, Colo., USA. In use, handheld devices fabricated according to the Dunne patent are manually directed toward, or precisely at, a target object and then manually “fired.” Once fired, the handheld device bounces a laser off the target object and measures the reflected laser energy that is then used to determine a retroreflectivity.
There are several drawbacks of the handheld laser arrangement described by the Dunne patent. The handheld device can only measure a single color at a time and can only measure one object at a time. The determination of retroreflectivity for a given object is valid only for the actual location, or discrete measurement point, along the roadway at which the measurement was made by the human operator. In order to validate a measurement made by such devices, the device must be taken back to the precise location in the field where an original measurement occurred for a valid comparison measurement to be made.
Another technique established for determining the nighttime visibility of signs has been introduced by the Federal Highway Administration (FHWA). The Sign Management and Retroreflectivity Tracking System (SMARTS) is a vehicle that contains one high intensity flash source (similar to the Honeywell StrobeGuard™ SG-60 device), one color camera, two black and white cameras, and a range-sensing device. The SMARTS vehicle requires two people for proper operation—one driver and one system operator to point the device at the target sign and arm the system. The SMARTS travels down the road, and the system operator “locks on” to a sign up ahead by rotating the camera and light assembly to point at the sign. At a distance of 60 meters, the system triggers the flash source to illuminate the sign surface, an image of which is captured by one of the black and white cameras. A histogram is produced of the sign's legend and background that is then used to calculate retroreflectivity. A GPS system stores the location of the vehicle along with the calculated retroreflectivity in a computer database.
Like the handheld laser device of the Dunne patent, the SMARTS device can only determine retroreflectivity for one sign at a time and can only determine retroreflectivity for the discrete point on the roadway 60 meters from the sign. Two people are required to operate the vehicle and measurement system. The SMARTS vehicle cannot make retroreflectivity determinations for signs on both sides of the roadway in a single pass over the roadway and does not produce nighttime sign visibility information for lanes on the roadway not traveled by the vehicle. Because the system operator in the SMARTS vehicle must locate and track signs to be measured while the vehicle is in motion, a high level of operational skill is required and the likelihood that a sign will be missed is significant.
There are an estimated 58 million individual TCDs that must be monitored and maintained in the United States and new TCD installations increase this number daily. For the reasons that have been described, the existing techniques for determining retroreflectivity do not lend themselves to increasing processing throughput so as to more easily manage the monitoring and maintenance of these TCDs. So called automated data collection systems often require that normal traffic be stopped during data collection because either the acquisition vehicle moved very slowly or because the acquisition vehicle had to come to a full stop before recording data about the roadside scene. Furthermore, a human operator is required to point one or more measurement devices at a sign of interest, perform data collection for that particular sign and then set up the device for another particular sign of interest. With such a large number of TCDs that must be monitored, it would be desirable to provide an automated system for determining the retroreflectivity of road signs and markers that addresses these and other shortcomings of the existing techniques to enable a higher processing throughput of an automated determination of the retroreflectivity of road signs and markers.
The present invention provides a system for the automated determination of retroreflectivity values for reflective surfaces disposed along a roadway. An area along the roadway that includes at least one reflective surface is repeatedly illuminated by a light source and multiple light intensity values are measured over a field of view which includes at least a portion of the area illuminated by the light source. A computer processing system is used to identify a portion of the light intensity values associated with a reflective surface and analyze the portion of the light intensity values to determine at least one retroreflectivity value for that reflective surface. Preferably, color images of the area and locational information are also generated by the system and are used together with a characterization profile of the light source to enhance the accuracy of the determination of retroreflectivity values. In one embodiment, a three-dimensional overlay of retroreflectivity values for the roadway is generated and can be manipulated to display retroreflectivity values of a reflective surface at any desired point along the roadway. In another embodiment, a virtual drive-through along a roadway is simulated using a plurality of retroreflectivity values to simulate reflections from each reflective surface disposed along the roadway during the virtual drive-through.
In contrast to the existing techniques for determining retroreflectivity that require an operator to target individual signs from a known distance, the present invention can determine retroreflectivity without targeting individual signs and can calculate retroreflectivity values at any desired point along a roadway. To overcome the limitations imposed by the existing techniques, the present invention employs several enhancements that are designed to improve the accuracy of evaluating intensity measurements made over a view where the reflective surfaces are not individually targeted and, therefore, neither the distance to the reflective surface or the normal vector to the reflective surface are known.
In a method in accordance with the present invention, retroreflectivity values for reflective surfaces disposed along a roadway are determined in an automated manner. A light source is strobed as the light source is traversed along the roadway to illuminate an area that includes at least one reflective surface. A plurality of light intensity measurements are collected using at least one intensity sensor directed to cover a field of view which includes at least a portion of the area illuminated by the light source. A computer processing system is then used to identify a portion of at least one light intensity measurement associated with one of the at least one reflective surfaces and analyze the portion of the at least one light intensity measurement to determine at least one retroreflectivity value for that reflective surface.
In a preferred embodiment of the method in accordance with the present invention, a characterization profile for the light source is created for this method. The characterization profile includes an array of known luminance values of reflections of the light source. The characterization profile for the light source is then utilized as part of determining the at least one retroreflectivity value for that reflective surface. Preferably, the array of known luminance values of reflection comprises reflected intensity values for the light source over a range of colors and reflected intensity values over a range of relative angles between the light source and the reflective surface. In one embodiment, a plurality of color images are captured using at least one color camera directed to cover a field of view which includes at least a portion of the area illuminated by the light source. The range of colors of the characterization profile for the light source and the plurality of color images are then used as part of determining the at least one retroreflectivity value for that reflective surface. In another embodiment, locational information is obtained for each of the plurality of light intensity measurements and used to determine a coordinate location for each reflective surface. The range of relative angles of the characterization profile for the light source and the coordinate location are then used as part of determining the at least one retroreflectivity value for that reflective surface. Preferably, a characterization profile of the light intensity sensor is also utilized to further enhance the accuracy of the system. The characterization profile for the intensity sensor preferably includes an array of intensity values of reflections as measured by the intensity sensor in response to a known light source.
A system for acquiring information to assess reflective surfaces disposed along a roadway in accordance with the present invention includes a vehicle and a computer processing system. The vehicle includes at least one high output light source, at least one intensity sensor, at least one color camera, a positioning system, and a control system. The control system is operably connected to the light source, intensity sensor, color camera and positioning system such that the intensity sensor, color camera and positioning system record information associated with an area that includes at least one reflective surface as the vehicle traverses along the roadway in response to repeated illumination of the area by the light source. The computer processor, which may reside within the vehicle or may be located separate from the vehicle, utilizes the recorded information to determine at least one retroreflectivity value for the at least one reflective surface.
In a preferred embodiment of the system, the vehicle further includes a laser scanning system that records distance information including at least a distance between the vehicle and each of the at least one reflective surfaces. The computer processing system utilizes the distance information generated by the laser scanning system to determine at least a normal vector for a face of the reflective surface. Preferably, the high output light source comprises at least two strobe lights arranged to alternatively illuminate the area at an effective strobe rate of at least one flash per second. Preferably, the intensity sensor comprises a black and white camera and the color camera comprises a pair of digital color cameras mounted on the vehicle to generate stereoscopic images of the area. The positioning system is preferably a global positioning system supplemented with an inertial navigation system. In the embodiment in which at least a portion of the computer processing system resides within the acquisition vehicle, at least a portion of the control system is preferably implemented using the computer processing system and a master clock supplied to all of the remaining components of the system to synchronize the system.
In another embodiment of the present invention, a method for displaying and manipulating retroreflectivity data for a reflective surface disposed along a roadway is provided. A plurality of retroreflectivity values for the reflective surface are determined, preferably using the method and system as described. A three-dimensional representation of retroreflectivity values of the reflective surface is generated and the three-dimensional representation of retroreflectivity values is displayed, preferably as an overlay over a depiction of a roadway. A simulation of the interaction of a vehicle operator/observer is accomplished by generating a simulated vehicle light source and a vehicle operator/observer pair and, for different locations of the simulated vehicle light source and said vehicle operator/observer pair, generating a simulated vehicle operator/observer observation angle and a simulated view of the vehicle pathway which includes the reflective surface. The simulation allows for simulating a changing magnitude of ambient lighting from a first value to a second value, or simulating a change of at least one characteristic of the depiction of the roadway. A corresponding change in the three-dimensional representation of retroreflectivity values as a result is determined and also simulated on the display. In one embodiment, the simulation is used to generate a new three-dimensional depiction of the reflective surface according to a predictive aging model which includes a gradual degradation of the reflective surface over time. The reflective surface can then be represented and simulated as if the reflective surface exhibited such a gradual degradation in the three-dimensional depiction.
In another embodiment, a method for simulating a virtual drive-through of a roadway that includes at least one reflective surface disposed along the roadway is provided. A plurality of retroreflectivity values are determined for each reflective surface. A virtual drive-through along the roadway is simulated using the plurality of retroreflectivity values to simulate reflections from each reflective surface disposed along the roadway during the virtual drive-through. Preferably, the virtual drive through allows for a simulation of a change of at least one characteristic of the depiction of the roadway, such that a corresponding change in the reflections from each reflective surface is determined and simulated. Preferably, the virtual drive-through simulates at least one vehicle having a light source and observer pair. In this embodiment, the virtual drive-through allows for a simulation of a change of at least one characteristic of the vehicle or light source and observer pair, such that a corresponding change in the reflections from each reflective surface is determined and simulated.
Retroreflectivity, designated as “RA” generally (and from time to time in this disclosure), varies according to two key parameters: observation angle and entrance angle. Observation angle 100 (See
Entrance angle 160 (See
The method of automatic determination of RA (See
The data required for the automated determination of RA is accumulated while traversing a highway 150 with the capture vehicle 225 (See
Characterization of sign 190 RA preferably utilizes the data recording system 260 to create a single tagged video stream 440 from the reflected light intensity frames 420, position measurements 350 and digital imagery 390 for each capture event 430 (See
For each object of interest 460, a background intensity measurement 470 and a foreground intensity measurement 480 is generated. Using an intensity algorithm 490, a light intensity sensor characterization 275 and a look-up-table 475, the computer processor 450 determines a background luminance value 500 and a foreground luminance value 510. Based on the background luminance value 500, the foreground luminance value 510, a characterization of light source wavelength 540, the background sheeting color 505 and the foreground sheeting color 506 the computer processor 450 characterizes a background RA 520 and a foreground RA 530 which are preferably reported separately for that object of interest.
The automated determination of multiple RA values for a given object of interest 460 allows for the extrapolation of RA values at an unmeasured viewing point 550 for an object of interest, such as a sign 190 (See
In one embodiment, the computer processor 450 can generate a simulated roadway 580 including roadway edges 585 and lane dividers 586 from information captured by the color imaging system 250 (See
An alternative embodiment of the present invention allows for the determination of retroreflectivity for pavement markings 630 (See
Pursuant to the teaching of the present invention, a method and apparatus for determining retroreflectivity of relatively flat surface portions of objects disposed adjacent a highway 150 traversed by a vehicle 140 are taught, enabled and depicted. The present invention may be utilized to detect and determine a retroreflective surface of interest disposed in a scene of non-retroreflective surfaces. That is, at least one object face surface 130 which exhibits retroreflectivity over at least a relatively narrow conical volume of magnitude of several degrees from a normal vector 180 originating from said object face surface 130.
In accordance with the present invention, a determination of the retroreflectivity of objects adjacent a highway 150 preferably includes (i) providing position measurements 350 of a capture vehicle 225; (ii) precise position of the object of interest 460, or sign 190; (iii) intensity measurements 300 from a high output light source 270 and light intensity sensor 280 at measurement intervals 430 along said highway 150. Thus, a single-pass along the highway 150 by the capture vehicle 225 operating the light intensity measurement system 230, vehicle positioning system 240, image capture system 250 and data recording system 260 taught herein eliminates many shortcomings of the prior art and allows a single vehicle operator to conduct virtually continuous data measurement and acquisition of objects of interest 460 disposed adjacent a highway 150, at capture events 430 on said highway 150, without disrupting or interrupting other vehicle traffic traversing said highway 150.
The image capture system 250 consists of at least one set of stereoscopic cameras 360 that gather digital imagery along the highway 150. Each capture event is combined with time stamp information from the vehicle positioning system 240 which also provides trigger control 227 for the image capture system 250 and trigger control 228 for the light intensity measurement system 230. These images and associated time stamps are later combined with photogrammetry to create objects of interest 460 and their associated attributes 465.
The light intensity measurement system 230 preferably consists of at least one high output light source(s) 270 and the associated light intensity sensor(s) 280. The precise control for these items is contained within the light intensity measurement system 230, and master time sequencing instrument 340 information received from the vehicle positioning system 240 (or computer processor 450) is combined to create a tagged videostream 440 so precise vehicle information can be utilized during post-processing.
The data recording system 260 is constantly monitoring control information from the other three on-board systems and records the necessary information. No post-processing is performed in the data recording system 260. As computer power increases in the future, one skilled in the art could produce a system whereby most, if not all, of the post-processing functions were performed in the capture vehicle 225, perhaps even in real-time. The inventors can imagine several uses for the production of real-time information from the image capture system 250 in the future, but the cost of obtaining such information with today's computing power makes this option prohibitively expensive today.
The lower half of
The high output light source(s) 270 and light intensity sensor(s) 280 constitute the light intensity measurement system 230. These components make it possible to gather on-the-fly information for a desired highway 150 to allow the computation of object of interest retroreflectivity 466, as well as create a full three-dimensional sign RA profile 590 for those same objects of interest 460.
The stereoscopic cameras 360 constitute the digital imagery system 390 that allows for the creation of objects of interest 460 and their associated attributes 465 during post-processing. More than one set of stereoscopic cameras 360 can be employed, thus increasing the accuracy of positional measurements for objects of interest 460. Other, non-stereoscopic imaging systems could also be employed with little or no change to the vehicle positioning system 240 or to the light intensity measurement system 230.
It should be noted that intensity measurements 300 are made continuously while the capture vehicle 225 is in motion, thus requiring no prior knowledge of either the positions or the existence of signs.
In order to compute retroreflectivity (RA), one needs to know the luminance of the reflected energy. Luminance (expressed in candelas per square meter, or cd/m2) will vary according to the intensity sensor characterization profile 275 of the light intensity sensor(s) 280 and the color of the material from which light is reflected.
Most roadway signs 190 contain text and/or symbols overlaid on a background. To ensure maximum visibility during day and night conditions, the colors of the foreground information (text and/or symbols) are chosen to have maximum day and night contrast with the background material. The techniques taught herein allow the retroreflectivity of roadway signs 190 to be determined for both foreground and background materials. Computing both the foreground 530 and background retroreflectivity 520 for each object of interest 460 allows us to ensure that the proper nighttime contrast is achieved for roadside assets. For example, a stop sign 190 with a red background and white lettering can provide good daytime contrast between the text and the sign background. But if these two materials display very similar retroreflectivity characteristics, their nighttime contrast will be minimal, thus rendering the sign ineffective during nighttime conditions.
The reader should note and appreciate that luminance is strictly a measure of the reflected light, while retroreflectivity (or RA, expressed in candelas/lux/m2) is a measure of the reflected light with respect to the incident light for that object.
To obtain the highest quality RA calculations, all of the data shown in
Since the beam from the high output light source 270 is diverging, objects of interest 460 farther from the origin of the light will receive less incident radiation than those objects of interest 460 closer to the light. The characterization of light source angle is constructed at a few discrete distances from the light. Simple geometry can be used to compute the incident radiation (using an interpolation method for an actual distance between two discrete distances in the characterization of light source angle) hitting the actual object of interest 460 based on the empirical data from the characterization of light source angle.
The preferred high output light source 270 is a uniform full-spectrum (visible spectrum) light. In practice, this light source will not emit the same intensity for all wavelengths of visible light. One variable of light source color characterization that should be considered is the output profile of the light throughout the visible spectrum.
The divergence pattern for the light source may have different profiles for various portions of the visible spectrum. In practice, a separate light source angle characterization profile may be required for each possible foreground and background color of any given object of interest 460.
A preferred high output light source 270 is of the type set forth in the attached installation and operation guide entitled “StrobeGuard™ High Intensity Obstruction Lighting System, Model No. SG-60,” manufactured by Honeywell, Inc. In order to create a useful three-dimensional sign RA profile 590 for an object of interest 460, intensity measurements 300 for frequent capture events 430 along a highway 150 while the capture vehicle 225 is in motion. For example, at vehicle speeds of 50 miles per hour, intensity measurements 300 should be taken at a rate of at least two per second. The StrobeGuard™ SG-60 model has a recharge time of about 1.5 seconds between successive flash events. As a result, one SG-60 light will not provide enough flash events per second to allow an adequate number of intensity measurements 300. In order to meet the requirements of two flash events per second for a capture vehicle 225 traveling at 50 miles per hour, three of the StrobeGuard™ SG-60 units would need to be fired in a synchronized, round-robin pattern to obtain enough trigger events.
The light intensity measurement system 230 described herein attempts to remove observation angle 100 as an RA variable. This is done by keeping the offset between the high output light source(s) 270 and light intensity sensor(s) 280 as low as possible. Once a simulated roadway 580 is created, observation angles 100 can be varied within the three-dimensional display software or within the virtual drive-through software to show their effects on the RA for the simulated roadway 580 and any as-placed sign 190.
As mentioned previously, a RA map of a simulated roadway 580 can be computed, even though the intensity was not measured at every point and even though the capture vehicle 225 did not drive over every point. First, it is critical that the geometry of RA is understood. Reflective materials like sign sheeting are designed to project near-columnated light back toward the light source. If a perfectly columnated light being reflected from the object of interest 460 being measured and a zero observation angle are assumed, the RA values for all discrete locations along a ray projected from the object will be identical.
If a perfectly columnated light is assumed, the value of RA at the desired point will be the same as the reference RA value. In practice, all sign 190 sheeting materials will have some beam divergence for reflected light. This beam divergence information can be used to adjust the computed RA value up (or down) from the reference RA value for discrete locations closer to (or farther from) the object's face surface 130.
While knowing the normal vector 180 to a sign 190 face is not required, there are some advantages for planning and maintenance purposes that make the information useful. Several ways to compute the normal vector 180 for a sign 190 exist. First of all, the “assumption” method requires that the normal vector 180 from the surface of the sign 190 is assumed to be parallel to the capture vehicle pathway 410 at the nearest location of the capture vehicle pathway 410 to the sign 190. Second, a scanning laser operating in conjunction with an optical sensor and having a common field of view may be used to more precisely resolve the normal vector 180 from the object's face surface 130. Third, stereoscopic cameras 360 may be employed in a useful, albeit very imprecise, manner of determining the normal vector 180. Fourth, the assumption method and stereo imaging method may be combined whereby the normal vector 180 is assumed to lie parallel to the vehicle pathway 410 unless the stereo imaging output renders the assumption false.
Of the methods listed above, the highest precision measuring systems for determining the normal vector 180 consists of a scanned laser and associated optical sensor. This combination yields relative distance measurements between the capture vehicle 225 and the object's face surface 130 that are more precise than optical measurements with cameras. A laser scanner attached to the capture vehicle 225 and directed toward a roadside scene populated with retroreflective signs 130 generates a series of reflection points to the optical sensor that appear as a horizontal segment of points. The optical sensor must be fast enough (i.e., have adequate data acquisition rates) to capture at least several individual discrete measurements across the object's face surface 130 (or of any other reflective asset). In general, two types of laser scanners are suitable to be utilized according to the present invention; namely, single-axis scanners and dual-axis scanners. A preferred sensor is of the type set forth in the proposal entitled, “Holometrics 3D Vision Technology,” as referenced in the previously identified provisional patent application.
Since most all types of roadside signs 190 to be measured are disposed at various elevations relative to the highway 150 and the capture vehicle 225, a single-axis laser scanner cannot be mounted such that the scanning laser beam covers only a single elevation or constant height relative to the highway 150 and the capture vehicle 225. Rather, the inventors hereof suggest that use of a single-axis type laser scanner must either be mounted high on the capture vehicle 225 with a downward facing trajectory, or be mounted low on the capture vehicle 225 with an upward facing scanning trajectory. These two mounting schemes for a single-axis laser scanner help ensure the lateral scan will intersect with virtually every object face surface 130 of all signs 190 or other objects of interest 460 present in a roadside scene regardless of the elevation or height or such signs relative to the roadway or to the moving platform.
Dual-axis laser scanners 335 circumvent the varying sign height problem inherently encountered if a single-axis laser scanner is employed as the source of integrated energy when practicing the teaching of the present invention. A dual-axis laser scanner 335 operates by continuously moving the scanning beam scan up and down at a relatively slow rate while sweeping the laser beam laterally from side to side across the field of view at a relatively more rapid rate.
In order to obtain the normal vector 180 for a sign 190 as taught hereunder, only a select horizontal series of discrete locations across the object's face surface 130 needs to be sensed by the high-speed optical sensor. For each point in the horizontal series of discrete locations recorded for a given sign 190 due to the incident radiation provided by the scanning laser, as sensed by the high speed optical sensor, the precise direction of the incident laser is recorded, thus allowing both distance and direction of the measured point to be determined.
Either of the scanning methods produces a massive number of sensed discrete locations representing discrete reflections of the incident laser radiation and each must be processed in order to correlate each of the sensed discrete locations with the object's face surface 130. Once the lateral series of discrete locations for a sign 190 is determined, simple triangulation methods are used to combine: (i) the vehicle location, (ii) vehicle heading vector, and (iii) scanned sign point to ultimately determine the normal vector 180 for the object's face surface 130.
As stated earlier, knowing the sign's 190 normal vector 180 can expand the utilization of the present invention. The retroreflective properties of sign 190 sheeting materials are typically symmetrical about the vertical axis of the object's face surface 130. Because of this symmetry, RA values (either computed or extrapolated/interpolated values) will be identical for rays that are symmetrical about the vertical axis.
While the image capture system 250 described herein can be used for locating objects of interest 460, it can also be used for mapping purposes. By creating representations of roadway edges and lane dividers, a very precise map can be created from the same digital imagery used for object of interest 460 creation. This mapping feature of the image capture system 250 will be a key component in the three-dimensional sign RA profile 590 mapping for signs and the virtual drive through, both to be discussed later.
The image capture system 250 and light intensity measurement system 230 are preferably free running, with measurements being made periodically during capture vehicle 225 operation. There is no requirement that these two systems be synchronized. In fact, these systems could operate in completely different capture vehicles 225, if necessary. When both systems are contained within the same capture vehicle 225, the only constraint for simultaneous operation is placed on the image capture system 250. Because of the intensity of the high output light source 270 in the light intensity measurement system 230, it is preferred that the image capture system 250 not capture frames at the same instant that the high output light source 270 is triggered. If images are actually captured while the high output light source 270 is triggered, their positional results would still be valid, but the colors displayed would be inaccurate because of the high output light being directed toward the (typically lower-thresholded) stereoscopic cameras 360.
One skilled in the art could completely eliminate any need for the image capture system 250 to know the firing events of the light intensity measurement system 230 by choosing sampling rates for the two systems that do not share any harmonic frequencies. On the rare occasions when the image capture system 250 captures images while the high output light source 270 is energized (or flashed), the skilled implementer could use time stamps to determine when this system simultaneity occurred and discard the imaging frames.
Producing a simulated roadway 580 map is an essential part of creating a three-dimensional sign RA profile 590 for a given vehicle 140 pathway. All maps of a highway 150 will contain some degree of error. Even survey-grade maps, which are expensive to create and cumbersome to build, may have errors of a few centimeters to a few inches or more. Maps created via other methods, such as those made from data gathered from capture vehicles 225, could have errors ranging from a few centimeters to several meters.
In practice, the simulated roadway 580 map created from the inventive image capture system 250 as disposed in or on a capture vehicle 225 and as otherwise described herein will be of higher value for the three-dimensional sign RA profile 590 than a survey-grade map. Even though the absolute errors may be greater in a two-dimensional or three-dimensional map created using the image capture system 250 of the present invention, they will be very small relative to the actual location of individual objects of interest 460 and also very small with respect to the discrete locations where the RA measurements—both computed and extrapolated or interpolated values—were made. The same systematic location errors may permeate all data points, but the relative accuracy will be high. Since RA is very dependent on the geometry of the objects of interest 460 and the magnitude and direction of the light incident thereon, a high degree of relative accuracy is more important than high absolute accuracy in creating a simulated roadway 580 base map of such all inventoried objects of interest 460.
If a sign 190 on the right hand side of the road is rotated on an axis perpendicular to a vehicle pathway one may not have enough computed RA points from the capture vehicle 225 given the track or path of such vehicle with which to compute the complete three-dimensional sign RA profile 590 for the entire highway 150 surface. However, if we know the normal vector 180 for the sign 190 (via any of the various methods described herein), one may use the typical horizontal symmetry of sign 190 sheeting materials to create the rest of the three-dimensional sign RA profile 590.
As mentioned earlier, observation angle 100 is a key determinant for sign 190 RA behavior. The light intensity measurement system 230 contained herein attempts to eliminate this variable by placing the high output light source(s) 270 and light intensity sensor 280 very close to one another. Planning departments can change observation angles 100 within the three-dimensional sign RA profile 590 software to understand its impact on sign 190 performance. Since the three-dimensional sign RA profile 590 software knows the as-measured sign 190 sheeting type and has available an RA performance profile (preferably embedded in a look up table of all popular or required sheeting types and/or as stored in memory, or dynamically generated for use in practicing the present invention), a new three-dimensional sign RA profile 590 can be generated for the newly-desired observation angle 100.
The aforementioned three-dimensional sign RA profile 590 is one way of utilizing the RA data to assess the object's of interest 460 performance. A “virtual drive-through” is another method of utilizing the computed RA data. Virtual drive-throughs are a popular tool used by transportation and planning departments. These drive-throughs consist of creating a three-dimensional volumetric model of a particular highway 150. Users of virtual drive-through applications can then move the view point along the highway 150 to understand what a driver (in a passenger vehicle, for example) would see as they traversed the selected highway 150. These drive-throughs, however, are usually modeled using information from the daytime scene.
Nighttime drive-throughs, on the other hand, can provide useful information beyond what can be learned from daytime drive-throughs. To properly represent nighttime drive-throughs, accurate information must be integrated regarding the nighttime performance of the objects of interest 460. As a result of the creation of an three-dimensional sign RA profile 590 for each object of interest 460, these objects of interest 460 assets can be placed in the virtual drive-through at their precise locations along the highway 150 (or on the highway 150 in the case of pavement markings 630). Utilizing nighttime asset performance in a virtual drive-through can highlight design or implementation problems that are not apparent with daytime virtual drive-throughs. Assessment of nighttime accidents can be significantly enhanced by using nighttime asset performance information in virtual drive-throughs.
Although the use of the three-dimensional sign RA profile 590 has been described with respect to RA values as determined by measurement of light intensity, it will be understood that the software for manipulating the three-dimensional sign RA profile 590 and the virtual drive-through can work equally as well in other modes where known data values are provided for some or all of the to RA values. For example, planning departments could insert RA values for known sheeting types of planned signs along a roadway in order to conduct a night time virtual drive through utilizing the software in accordance with the present invention to manipulate the location and placement of such signs to achieve better visibility.
Reflective pavement markings 630 display similar properties to objects of interest 460 (and other reflective objects), but the determination of pavement marking 630 retroreflectivity requires some extra constraints. Pavement marking 630 retroreflectivity is expressed in milli-candelas per square meter per lux (mcd/m2/lux) and is designated RL. Along with observation angle 100 and entrance angle 160, the lateral distance 220 between the light source 110 and the pavement markings 630 must also be known.
Studies have shown that there is no correlation between pavement marking 630 retroreflectivity (RL) values with different geometries. Because of this lack of correlation, agreed-upon geometries must be utilized when measuring intensity and reporting RL. When measuring intensity and computing RA the present invention attempts to dramatically reduce (essentially eliminate) observation angle 100 with the high output light source 270 and light intensity sensor 280 placement. When measuring intensity for computing RL, it is preferable to position the high output light source 270 and the light intensity sensor 280 such that the agreed-upon geometry is met for the desired measurement distance. At the time of this disclosure, both the European Committee for Normalization and the American Society for Testing and Materials (ASTM) have standardized on the same RL geometry of 1.05 degree observation angle 100, 88.76 degree entrance angle 160, and 30 meter measurement distance 220.
The intensity of light reflected from pavement markings 630 will be less than that of signs 190. As a result, another light intensity sensor 280 may be needed for the determination of RL. Therefore, there are two reasons for requiring a different light intensity sensor 280 for determining RA and RL—the range of intensities and the placement of the light intensity sensor 280. However, the same high output light source(s) 270 can be used for determining RA and RL.
The present invention has been described with respect to particular illustrative embodiments. It is to be understood that the invention is not limited to the above-described embodiments and modifications thereto, and that various changes and modifications may be made by those of ordinary skill in the art without departing from the spirit and scope of the appended claims.
|Cited Patent||Filing date||Publication date||Applicant||Title|
|US3636250 *||Feb 26, 1964||Jan 18, 1972||Andrew V Haeff||Apparatus for scanning and reproducing a three-dimensional representation of an object|
|US5164785 *||Feb 8, 1991||Nov 17, 1992||Hopkins Manufacturing Corporation||Headlight aiming apparatus and display|
|US5373357 *||Oct 21, 1992||Dec 13, 1994||Hopkins Manufacturing Corporation||Headlight aiming method using pattern framing|
|US5392365||Dec 23, 1991||Feb 21, 1995||Eastman Kodak Company||Apparatus for detecting text edges in digital image processing|
|US5448484||Nov 3, 1992||Sep 5, 1995||Bullock; Darcy M.||Neural network-based vehicle detection system and method|
|US5465115||Jan 10, 1995||Nov 7, 1995||Rct Systems, Inc.||Video traffic monitor for retail establishments and the like|
|US5465308||Aug 25, 1993||Nov 7, 1995||Datron/Transoc, Inc.||Pattern recognition system|
|US5627915||Jan 31, 1995||May 6, 1997||Princeton Video Image, Inc.||Pattern recognition system employing unlike templates to detect objects having distinctive features in a video field|
|US5633944||Apr 19, 1995||May 27, 1997||Automobiles Peugeot||Method and apparatus for automatic optical recognition of road signs|
|US5633946||Nov 3, 1995||May 27, 1997||Geospan Corporation||Method and apparatus for collecting and processing visual and spatial position information from a moving platform|
|US5699444||Mar 31, 1995||Dec 16, 1997||Synthonics Incorporated||Methods and apparatus for using image data to determine camera location and orientation|
|US5740274||Aug 29, 1994||Apr 14, 1998||Fuji Photo Film Co., Ltd.||Method for recognizing object images and learning method for neural networks|
|US5790691||Mar 12, 1996||Aug 4, 1998||The Regents Of The University Of Colorado||Method and apparatus for robust shape detection using a hit/miss transform|
|US5818640 *||Jul 27, 1995||Oct 6, 1998||Minnesota Mining And Manufacturing Company||Sign illumination system and method|
|US5844699||Jun 7, 1995||Dec 1, 1998||Canon Kabushiki Kaisha||Color image processing apparatus|
|US5864630||Nov 20, 1996||Jan 26, 1999||At&T Corp||Multi-modal method for locating objects in images|
|US5938319 *||Jun 17, 1996||Aug 17, 1999||Robert Bosch Gmbh||Vehicle headlight with adjusting means for different traffic conditions|
|US5974521||Mar 31, 1998||Oct 26, 1999||Neomagic Israel Ltd.||Apparatus and method for signal processing|
|US5991085||Jul 12, 1996||Nov 23, 1999||I-O Display Systems Llc||Head-mounted personal visual display apparatus with image generator and holder|
|US6011515 *||Oct 6, 1997||Jan 4, 2000||The Johns Hopkins University||System for measuring average speed and traffic volume on a roadway|
|US6023967 *||Oct 10, 1997||Feb 15, 2000||Kumho Tire Co., Ltd.||Method for measuring tire wear using intensity of reflected light|
|US6048069||Mar 9, 1999||Apr 11, 2000||3M Innovative Properties Company||Wide angle image-displaying sheet and system|
|US6064768||Jun 30, 1997||May 16, 2000||Wisconsin Alumni Research Foundation||Multiscale feature detector using filter banks|
|US6123263 *||Jan 29, 1998||Sep 26, 2000||Meta Holdings Corporation||Hand held dataform reader having strobing ultraviolet light illumination assembly for reading fluorescent dataforms|
|US6134819 *||Feb 11, 1998||Oct 24, 2000||Mcclain; Ronald G.||Portable illuminated traffic signal device|
|US6141433||Dec 24, 1997||Oct 31, 2000||Ncr Corporation||System and method for segmenting image regions from a scene likely to represent particular objects in the scene|
|US6166813||Apr 16, 1998||Dec 26, 2000||Georgia Tech Research Corporation||Retroreflectometer and method for measuring retroreflectivity of materials|
|US6212480||Jan 25, 2000||Apr 3, 2001||Laser Technology, Inc.||Apparatus and method for determining precision reflectivity of highway signs and other reflective objects utilizing an optical range finder instrument|
|US6240664 *||Nov 2, 1998||Jun 5, 2001||Airlite Unlimited||Illuminated sign and sign plate therefor|
|US6253477 *||Sep 23, 1998||Jul 3, 2001||Hallmark Technologies, Inc.||Retro-reflective sign|
|US6266442||Oct 23, 1998||Jul 24, 2001||Facet Technology Corp.||Method and apparatus for identifying objects depicted in a videostream|
|US6382126 *||Sep 28, 2000||May 7, 2002||Craig Alan Findley||Reflective and adhesive patch|
|US6407674 *||Mar 5, 1998||Jun 18, 2002||Philip Anthony Gallagher||Reflectivity measuring apparatus and method|
|US6476910 *||Aug 29, 2000||Nov 5, 2002||The Regents Of The University Of California||Light scattering apparatus and method for determining radiation exposure to plastic detectors|
|US6507441 *||Oct 16, 2000||Jan 14, 2003||Optid, Optical Identification Technologies Ltd.||Directed reflectors and systems utilizing same|
|US6538751 *||Feb 16, 2001||Mar 25, 2003||Fuji Photo Film Co., Ltd.||Image capturing apparatus and distance measuring method|
|US6558021 *||Aug 10, 2001||May 6, 2003||Leotek Electronics Corporation||Light emitting diode modules for illuminated signs|
|US6594931 *||Aug 21, 2000||Jul 22, 2003||Jeffrey C. Barton||Fluorescent illuminated sign assembly|
|US6674878 *||Jul 30, 2001||Jan 6, 2004||Facet Technology Corp.||System for automated determination of retroreflectivity of road signs and other reflective objects|
|US6772062 *||Jan 8, 2002||Aug 3, 2004||The Regents Of The University Of California||Intelligent ultra high speed distributed sensing system and method for sensing roadway markers for intelligent vehicle guidance and control|
|US6885767 *||May 11, 2001||Apr 26, 2005||Paul Howell||Silhouetting apparatus and method|
|US6888622 *||Feb 3, 2003||May 3, 2005||Nissan Motor Co., Ltd.||Method for determining object type of reflective object on track|
|US6891960 *||Aug 10, 2001||May 10, 2005||Facet Technology||System for road sign sheeting classification|
|US20010021011 *||Feb 16, 2001||Sep 13, 2001||Shuji Ono||Image capturing apparatus and distance measuring method|
|US20010045034 *||Jul 13, 2001||Nov 29, 2001||Lance Mueller||Illuminated sign and method for design|
|US20020044278 *||Jul 13, 2001||Apr 18, 2002||Tuan Le||Apparatus for optical inspection of a working surface having a dynamic reflective spatial attenuator|
|US20020063638 *||Mar 5, 1998||May 30, 2002||Philip Anthony Gallagher||Reflectivity measuring apparatus and method|
|US20020106109 *||Aug 10, 2001||Aug 8, 2002||Retterath James E.||System for road sign sheeting classification|
|US20020163942 *||Dec 20, 2001||Nov 7, 2002||Applied Optoelectronics, Inc.||Multiple reflectivity band reflector for laser wavelength monitoring|
|US20020186865 *||Jul 30, 2001||Dec 12, 2002||Facet Technology, Inc.||System for automated determination of retroreflectivity of road signs and other reflective objects|
|US20030174054 *||Feb 3, 2003||Sep 18, 2003||Nissan Motor Co., Ltd.||Method for determining object type of reflective object on track|
|US20040127614 *||Oct 15, 2003||Jul 1, 2004||Peijun Jiang||Polyolefin adhesive compositions and articles made therefrom|
|US20050249378 *||Feb 11, 2005||Nov 10, 2005||Facet Technology, Inc.||System for road sign sheeting classification|
|1||A Real-Time Traffic Sign Recognition System, S. Estable, J. Schick, F. Stein, R. Janssen, R. Ott, W. Ritter, Y.-J. Zheng, Daimler-Benz Research Center, Proceedings of the Intelligent Vehicles '94 Symposium, Paris, France, pp. 213-218, Oct. 1994.|
|2||A Texture-based Object Detection and an Adaptive Model-based Classification, T. Kalinke, C. Tzomakas, W. Seelen, Institut für Neuroinformatik, Bochum, Germany, IEEE International Conference on Intelligent Vehicles, pp. 143-148, 1998.|
|3||A Trainable Pedestrian Detection System, C. Papageorgiou, T. Evgenious, T. Poggio, Center for Biological And Computational Learning and Artificial Intelligence Laboratory, MIT, IEEE International Conference on Intelligent Vehicles, pp. 241-246, 1998.|
|4||An Architecture of Object Recognition System for Various Images Based on Multi-Agent, Keiji Yanai, Koichiro Deguchi, Dept. of Computer Science, University of Electro-Communications, Tokyo, Japan, and Dept. of Mathematical Engineering and Information Physics, University of Tokyo, Tokyo, Japan, 4 pgs., undated.|
|5||Auto-associative Segmentation for Real-Time Object Recognition in Realistic Outdoor Images, Leonardo Estevez and Nasser Kehtarnavaz, Dept. of Electricial Engineering, Texas A&M University, Journal of Electronic Imaging, vol. 72, pp. 378-385, Apr. 1998.|
|6||Dominant Color Transform and Circular Pattern Vector for Traffic Sign Detection and Recognition, Jung Hak and Tae Young Choi, IEICE Transaction Fundamentals, vol. E81-A, No. 6, pp. 1128-1135, Jun. 1998.|
|7||Feature Integration and Relevancy Feedback Analysis in Image Similarity Evaluation, Celentano, Journal of Electronic Imaging, vol. 7 (2), pp. 308-317, Apr. 1998.|
|8||Illumination Invariant Image Indexing Using Moments and Wavelets, Mandal, Journal of Electronic Imaging, vol. 7 (2), pp. 282-293, Apr. 1998.|
|9||Internet Printout: The Chamfer System, 4 pgs., c. approximately 1999.|
|10||Internet Printout: The Road Sign Recognition System-RS<SUP>2 </SUP>, Faculty of Transportation Sciences, Prague, Czech Republic, 7 pgs., c. approximately 1999.|
|11||Landmark Recognition using Projection Learning for Mobile Robot Navigation, Ren C. Luo, Harsh Potlapalli, Center for Robotics and Intelligent Machines, IEEE World Congress on Computational Intelligence, vol. IV, pp. 2703-2708, Jun. 1994.|
|12||Multi-Feature Matching Algorithm for Free-Form 3D Surface Registration, C. Schütz, T. Jost, H, Hügli, Institute for Microtechnology, Neuchatel, Switzerland, 3 pgs., undated.|
|13||Multi-Modal Tracking of Faces for Video Communications, James L. Crowley and Francois Berard, GRAVIR-IMAG, I.N.P. Grenoble, Grenoble, France, IEEE, pp. 640-645, 1997.|
|14||Real-Time Object Recognition: Hierarchical Image Matching in a Parallel Virtual Machine Environment, J. You, P. Bhattacharya, S. Hungenahally, School of Computing and Information Technology, Griffith University, Brisbane, Australia, Dept. of Computer Engineering, University of Nebraska, Lincoln, Nebraska, 3 pgs., undated.|
|15||Realtime Traffic Sign Recognition (TSR), Jens Logemann, Ed., Univeritat Koblenz-Landau, 3 pgs., Nov. 1997.|
|16||Recognition of Traffic Signs by Artificial Neural Network, D. Ghica, S. Lu, X. Yuan, Dept. of Computer Science Memorial University of Newfoundland, IEEE, pp. 1444-1449, Mar. 1995.|
|17||Registering Multiple Cartographic Models with the Hierarchial Mixture of Experts Algorithm, Simon Moss and Edwin R. Hancock, Dept. of Computer Science, University of New York, IEEE, pp. 909-914, 1997.|
|18||Representation of Uncertainty in Spatial Target Tracking, Tim Baker, Malcolm Strens, DERA Farnborough, United Kingdom, 4 pgs., undated.|
|19||Road Traffic Sign Detection and Classification, A. Escalera, L. Moreno, M. Salichs, J. Armingol, IEEE Transactions on Industrial Electronics, vol. 44, No. 6, pp. 848-859, Dec. 1997.|
|20||Robust Lane Recognition Embedded in a Real-Time Driver Assistance System, R. Risack, P. Klausmann, W. Krüger, W. Enkelmann, Fraunhofer-Institut für Informations, Karlsruhe, Germany, IEEE International Conference on Intelligent Vehicles, pp. 35-40, 1998.|
|21||Surveillance Systems for Terrestrial Transport Safety and Improved User Information Capability, C. Nwagboso, C. Regazzoni, M. Renard, E. Stringa, Bolton Institute, Bolton, United Kingdom, Dept. of Biophysical & Electronic Engineering, Genova, Italy, Vigitec, Brussels, Belgium, pp. 1-7, undated.|
|22||Using Centroid Covariance in Target Recognition, Gang Liu and Robert M. Haralick, Dept. of Electrical Engineering, University ofWashington, Seattle, Washington, 4 pgs., undated.|
|23||Using Spatial Sorting and Ranking in Model Based Object Recognition, G. Hjaltason, M. Ray, H. Samet, I. Weiss, Computer Science Dept. University of Maryland, College Park, Maryland, 3 pgs., undated.|
|Citing Patent||Filing date||Publication date||Applicant||Title|
|US7411681||Feb 5, 2007||Aug 12, 2008||Facet Technology Corporation||System for automated determination of retroreflectivity of road signs and other reflective objects|
|US7623248||Jan 19, 2007||Nov 24, 2009||Geo-3D Inc.||Automatic asset detection, location measurement and recognition|
|US7941269||Nov 11, 2008||May 10, 2011||Rialcardo Tice B.V. Llc||Network-based navigation system having virtual drive-thru advertisements integrated with actual imagery from along a physical route|
|US7995796||Apr 7, 2009||Aug 9, 2011||Facet Technology Corp.||System for road sign sheeting classification|
|US8150216||Sep 14, 2009||Apr 3, 2012||Google Inc.||Methods and apparatus for automated true object-based image analysis and retrieval|
|US8233670||Dec 31, 2007||Jul 31, 2012||Cognex Corporation||System and method for traffic sign recognition|
|US8406992||Dec 17, 2010||Mar 26, 2013||Rialcardo Tice B.V. Llc||Network-based navigation system having virtual drive-thru advertisements integrated with actual imagery from along a physical route|
|US8660311||Aug 8, 2011||Feb 25, 2014||Facet Technology Corp.||System for assessment reflective objects along a roadway|
|US8860944||Sep 12, 2013||Oct 14, 2014||Facet Technology Corp.||System and assessment of reflective objects along a roadway|
|US8903199||Feb 6, 2012||Dec 2, 2014||Google Inc.||Methods and apparatus for automated true object-based image analysis and retrieval|
|US8908996||Jan 31, 2012||Dec 9, 2014||Google Inc.||Methods and apparatus for automated true object-based image analysis and retrieval|
|US8908997||May 29, 2014||Dec 9, 2014||Google Inc.||Methods and apparatus for automated true object-based image analysis and retrieval|
|US9091628||Dec 21, 2012||Jul 28, 2015||L-3 Communications Security And Detection Systems, Inc.||3D mapping with two orthogonal imaging views|
|US9171360||Apr 8, 2008||Oct 27, 2015||DBI/CIDAUT Technologies, LLC||Method for determining the luminance of traffic signs and device for implementing same|
|US9171362||Apr 30, 2014||Oct 27, 2015||DBI/CIDAUT Technologies, LLC||Method for determining the luminance of traffic signs and device for implementing same|
|US9335255||Oct 13, 2014||May 10, 2016||Facet Technology Corp.||System and assessment of reflective objects along a roadway|
|US9336627 *||Mar 11, 2013||May 10, 2016||Hntb Holdings Ltd.||Creating a model of a scanned surface for comparison to a reference-surface model|
|US9424277||Oct 31, 2014||Aug 23, 2016||Google Inc.||Methods and apparatus for automated true object-based image analysis and retrieval|
|US9671328||May 6, 2016||Jun 6, 2017||Facet Technology Corp.||System and assessment of reflective objects along a roadway|
|US20070171431 *||Jan 19, 2007||Jul 26, 2007||Claude Laflamme||Automatic asset detection, location measurement and recognition|
|US20070216904 *||Feb 5, 2007||Sep 20, 2007||Facet Technology Corporation||System for automated determination of retroreflectivity of road signs and other reflective objects|
|US20090074249 *||Dec 31, 2007||Mar 19, 2009||Cognex Corporation||System and method for traffic sign recognition|
|US20090225165 *||Dec 5, 2008||Sep 10, 2009||Goodrich Corporation||Precision optical systems with performance characterization and uses thereof|
|US20090252376 *||Apr 7, 2009||Oct 8, 2009||Retterath Jamie E||System for Road Sign Sheeting Classification|
|US20100316252 *||Apr 8, 2008||Dec 16, 2010||Fundacion Cidaut||Method For Determining The Luminance Of Traffic Signs And Device For Implementing Same|
|US20130238305 *||Mar 11, 2013||Sep 12, 2013||Hntb Holdings Ltd.||Creating a model of a scanned surface for comparison to a reference-surface model|
|U.S. Classification||382/104, 382/106, 382/162, 359/627|
|International Classification||G08G1/0962, G06K9/00, G08G1/0967|
|Cooperative Classification||G08G1/096783, G08G1/096775, G01N2021/551, G08G1/0962, G08G1/096758, G08G1/096716, G01N21/55, G08G1/096791|
|European Classification||G01N21/55, G08G1/0967A1, G08G1/0967B3, G08G1/0967C3, G08G1/0967C1, G08G1/0962, G08G1/0967C2|
|Nov 9, 2009||FPAY||Fee payment|
Year of fee payment: 4
|Jun 28, 2012||AS||Assignment|
Owner name: FACET TECHNOLOGY CORP., MINNESOTA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:RETTERATH, JAMIE;LAUMEYER, ROBERT;REEL/FRAME:028458/0632
Effective date: 20120627
|Nov 12, 2013||FPAY||Fee payment|
Year of fee payment: 8
|Sep 5, 2014||AS||Assignment|
Owner name: DBI/ CIDAUT TECHNOLOGIES, LLC, PENNSYLVANIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:FACET TECHNOLOGY CORP;REEL/FRAME:033674/0966
Effective date: 20140729
|Sep 8, 2014||AS||Assignment|
Owner name: DBI/CIDAUT TECHNOLOGIES, LLC, PENNSYLVANIA
Free format text: CORRECTIVE ASSIGNMENT TO CORRECT THE ASSIGNOR'S NAME PREVIOUSLY RECORDED AT REEL: 033674 FRAME: 0966. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT;ASSIGNOR:FACET TECHNOLOGY CORP.;REEL/FRAME:033696/0232
Effective date: 20140729
|Feb 23, 2016||AS||Assignment|
Owner name: AMAC NORTH AMERICA, LLC, PENNSYLVANIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:DBI HOLDING, LLC;REEL/FRAME:037796/0292
Effective date: 20160212
Owner name: AMAC NORTH AMERICA, LLC, PENNSYLVANIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:CIDAPI, S.L.;REEL/FRAME:037796/0334
Effective date: 20160212
Owner name: DBI HOLDING, LLC, PENNSYLVANIA
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:DBI/CIDAUT TECHNOLOGIES, LLC;REEL/FRAME:037798/0311
Effective date: 20160212
Owner name: CIDAPI, S.L., SPAIN
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:DBI/CIDAUT TECHNOLOGIES, LLC;REEL/FRAME:037798/0311
Effective date: 20160212
|Sep 19, 2017||MAFP|
Free format text: PAYMENT OF MAINTENANCE FEE, 12TH YR, SMALL ENTITY (ORIGINAL EVENT CODE: M2553)
Year of fee payment: 12